Robust Haptic Rendering Using a Nonlinear Impedance Matching Approach (NIMA) for Robotic Laparoscopic Surgery

Background: The integration of haptic feedback into robot-assisted minimally invasive surgery (RAMIS) has long been limited by challenges in accurately rendering forces and ensuring system safety. The need for robust, high-fidelity haptic systems is critical for enhancing the precision and reliability of teleoperated surgical tools. Methods: In this study, we present a Nonlinear Impedance Matching Approach (NIMA) designed to improve force rendering by accurately modelling complex tool-tissue interactions. Based on our previously validated Impedance Matching Approach (IMA), our novel NIMA method includes nonlinear dynamics to capture and render tool-tissue forces effectively. Results: NIMA improves force feedback accuracy with a mean absolute error (MAE) of 0.01 (SD 0.02) N, achieving a 95% reduction in MAE compared to IMA. Furthermore, NIMA effectively eliminates haptic "kickback" by ensuring no force is applied by the haptic device to the user's hand when they release the handle, enhancing both patient safety and user comfort. Conclusion: NIMA's ability to account for nonlinearities in tool-tissue interactions provides an improvement in force fidelity, responsiveness, and precision across various surgical conditions. Our findings promote the advancement of haptic feedback systems for robotic surgery, offering a realistic and reliable interface for robot-assisted surgical procedures.